AiFi and the Dawn of a Decentralized AI Economy
“This is exactly how Lisan al Gaib would implement GPU tokenization”
Hey gang, Chris here. Although ETHDenver was a month ago, I had mentioned in my initial overview that I wanted to do a couple of deep dive articles on a few projects, because I felt like they deserved additional attention. These are the projects that really got my attention in February.
One of these projects is GAIB, which markets itself as the first economic layer for AI & compute. Even since early 2024, many projects have pivoted and begun marketing themselves as web3 x AI platforms. I was genuinely curious what substance or validity there is to the ideas they’re posing, so I dug in.
So, what is AiFi, and why should you care?
AiFi Summit Denver
This one day summit took place at the Sheraton Denver downtown hotel on February 28th, and included a few notable projects and partners (Base, Sahara AI, Near, Sentient, and Google to name a few). Themes included discussions around compute scarcity, tokenized GPUs, and the potential of AI agents within DeFi. These aren’t new ideas, but the projects were new to me.
Let’s break down what each project at the summit is all about:
GAIB: a platform that tokenizes GPUs and their compute power, turning them into yield-bearing assets. It enables broader ownership of AI infrastructure and financializes access to the growing AI and compute economy. GAIB can run on multiple blockchains, including Ethereum and L2s like Optimism.
PIN AI: building a Personal Intelligence Network that gives users full ownership and control of their AI agents. It uses crypto-economic security to ensure privacy and enables a wide range of open-source AI applications.
Kite AI: a decentralized infrastructure designed for AI collaboration. It uses Proof of AI to support transparent, fair interaction across data, models, and autonomous agents.
These projects have a good amount of overlap. They all agree that the future is distributed, co-owned, and open for all. That means full-ownership, transparency, and the usage of autonomous agents to open up new possibilities in the decentralized economy. For this article, we’re going to focus solely on GAIB.
Key Topics: Compute, Tokenization, and Funding
AiFi: a vision to integrate AI and finance (AiFi) through blockchain technology, specifically by tokenizing graphics processing units (GPUs) and their yields.
Unless you’ve been sleeping under a rock, you know that GPUs are the lifeblood of AI. Everyone is hungry for compute, and Nvidia can’t make their graphics cards fast enough. As one panel pointed out, it is increasingly difficult for companies outside of the top 5 big tech giants to access these critical resources. This has been echoed time and time again, and is a sentiment held by many in the industry.
How can the little guys compete when Amazon is throwing easily 100 billion dollars a year at chips?
GAIB’s take is straightforward. Tokenize GPUs, turn their yields into a new type of asset class, and give regular folks a chance at owning a piece. Think fractional real-estate, but for AI horsepower. If compute is the new oil, we need to be able to trade it in a similar way.
Financializing compute isn’t as simple as slapping tokens onto hardware and calling it a day. A key component of this is structuring deals with cloud providers and data centers, packaging GPU cash flows, and putting them on-chain.
This is the idea fueling USDai, a synthetic dollar backed by those yields (I’m not clear what stabilizes the peg). Passive income meeting AI infra, with users earning, not computing.
As GAIB argues, there is a missing piece in the supply and demand for decentralized compute. Companies like Nvidia and Intel cover the hardware layer, and networks like Akash provide access to utilizing GPUs. Adding in a GPU tokenization layer enables direct compute asset exposure in the GPU asset class. For investors, that means a reduced barrier to entry, further democratizing and decentralizing the market. The AI economy flywheel speeds up by reducing barriers for all stakeholders.
Compute is the new currency, GPUs are the new asset, and currently companies like Nvidia capture most of the value in the GenAI supply chain. Hardware, then infrastructure, then apps. As one speaker during the conference said “for every dollar spent on an H100 you get seven bucks in revenue over four years’. I like the simplicity of that.
Funding in a traditional sense is also a concern. Cloud companies and data centers are scrambling to scale for AI, and traditional finance is slow. GAIB aims to flip the script, and channel capital through tokenized assets, having a yield-bearing asset that is accessible, and not just for whales.
GAIB works in two steps:
Financialization: They strike deals with cloud providers, financialize GPU cash flows (think interest or revenue shares), and tokenize them. No hardware ownership—just the profits, packaged into USDai.
Liquidity: They plug these tokens into DeFi—AMMs, lending protocols, yield trading platforms like Pendle. It’s a marketplace where AI agents and humans alike can trade, stake, or leverage compute yields.
This isn’t without challenges. Enterprise companies are still not a huge fan of crypto or tokenization. The workaround may be to focus on savings and accessibility, and get people talking to enterprises that can bridge the gap. We need CTOs to understand tokenized compute. And lastly, regulation. Some countries like Singapore are ahead of the curve, but most aren’t. It’ll take some time, years, for the world to catch up and understand what is happening here.
GAIB operates at the intersection of AI, tokenized RWAs, and DeFi - tapping into massive, fast-growing markets:
Tokenized GPUs & Yields - $2T TAM
Cloud compute is projected to reach $2T by 2030. GAIB turns GPUs into yield-generating assets to meet AI demand (by brokering access to idle GPU time and channeling cash flow to token holders).GPU-Backed Stablecoins - $200B TAM
Stablecoins are a $200B market. USDai offers a yield-bearing, liquid alternative tied to AI compute.Lending & Borrowing - $600B TAM
Asset-backed lending is a $600B market. GPUs serve as high-quality collateral for capital-efficient borrowing.Options & Futures - $54T TAM
Gold futures trade $54T annually. Tokenized GPUs unlock similar derivative markets for AI infrastructure.Structured Products & Derivatives - $700T TAM
The $700T OTC derivatives market can be extended to GPU-backed products - enabling new tools for hedging, yield, and speculation in AI.
Final Thoughts on the Spice
Where do we see ourselves in the year 2030? AI will proliferate, and AGI seems likely. Are we destined for a massive, heterogeneous collection of hardware? Not just data centers, but consumer devices, all plugged into decentralized networks? Will illiquid hardware assets become tradable and yield-generating financial instruments?
Personally, I think this idea will catch on. Agents will buy and sell compute faster than people can perceive, and many of us will have largely automated wallets that perform DeFi (I mean AiFi) actions on our behalf.
We’re witnessing a new economy being built, and I’m excited to see where this goes. If you care about AI, decentralization, or owning the rails of tomorrow, this is worth watching.
I’ll be back in a couple of days with another deep-dive on a project that caught my attention.
Have a great weekend.
- Chris